PhD Chapter 3

Results 3/3


This series of files compile all analyses done during Chapter 3:

All analyses have been done with R 4.0.4.

Click on the table of contents in the left margin to assess a specific analysis.
Click on a figure to zoom it

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Sources of activity considered for the analyses:

Fisheries data considered for the analyses (expressed as number of fishing events or kilograms of collected individuals for each gear):

Gear Code Years Events Species
Dredge FishDred 2010-2014 21 Mactromeris polynyma
Net FishNet 2010 5 Clupea harengus, Gadus morhua
Trap FishTrap 2010-2015 1061 Buccinum sp., Cancer irroratus, Chionoecetes opilio, Homarus americanus
Bottom-trawl FishTraw 2013-2014 2 Pandalus borealis

1. Methodology

The aim of this section is to predict the structure of benthic communities based on the values of environmental variables.

We used abiotic parameters and indices of human exposure indices (calculated in Section 1) as predictors. We tested different methods: GLMs, GAMs, Random Forest and HMSC. Each method has been developed in dedicated scripts, whose final objects were imported here to present results and trends.

For each method, results are presented with a table regrouping McFadden’s or Tjur’s pseudo-R2, validation ratios and variables coefficients, and with maps displaying the probability of presence of each taxon for which the pseudo-R2 is higher than 0.20 (and different than 1). The raster presents results of the SDM (grey: low probability, dark blue: high). Stations are either plotted with colors (green = taxa present, red = taxon absent) or with circles (wider circles = higher taxon density).

2. Models

2.1. Generalized Linear Models

Diagnostics for each model can be found here (presence/absence) and here (density).

2.1.1. Presence/absence data

We considered presence/absence data with a binomial distribution.

All parameters
Abiotic parameters
Exposure indices

2.1.2. Density data

⚠️ To be added … or not

2.2. Hierarchical Models of Species Communities

This section uses methodology and tools from Ovaskainen et al., with the direct help of Guillaume Blanchet.

First, we will compute models using the 108 stations with abiotic variables or exposure indices as predictors. 85 % of the stations (92) will act as training data, and the rest (16) will be used to validate the outputs. Second, these models will be used to predict taxa richness and distribution in the entire study area using predictor rasters.

We initiate the HMSC model with the chosen data:

  • presence/absence or density for dependant variable
  • exposure indices and/or abiotic variables for predictors

Priors and model parameters are set in the hmsc() function.

HMSC_PA <- hmsc(data, param, prior, family = "probit", niter = 100000, nburn = 1000, thin = 100)
HMSC_density <- hmsc(data, param, prior, family = "overPoisson", niter = 100000, nburn = 1000, thin = 100)

Here are the outcomes and diagnostics to evaluate each model’s quality (presented for each species seperately or averaged).

Diagnostics for each taxon can be found here (presence/absence) and here (density).

2.2.1. Presence/absence data

We considered presence/absence data with a probit distribution.

All variables
Predictor coefficients

Mean of the predictor coefficients estimated by the HMSC model:

95 % confidence interval of the predictor coefficients estimated by the HMSC model:

Predictive power

Variance partitioning

Global diagnostics

Individual taxa:

Averaged taxa:

2.2.2. Density data

We considered density data with an overPoisson distribution.

All variables
Predictor coefficients

Mean of the predictor coefficients estimated by the HMSC model:

95 % confidence interval of the predictor coefficients estimated by the HMSC model:

Predictive power

Variance partitioning

Global diagnostics

Individual taxa:

Averaged taxa:

3. Predictions of species richness

3.1. Generalized Linear Models

All variables

Prediction of specific richness based on this model:

Difference between predicted and observed taxa richness
station_id observed predicted difference
127 1 13 12
128 12 14 2
129 15 14 -1
130 4 13 9
131 10 14 4
132 10 32 22
134 9 8 -1
135 8 41 33
136 9 18 9
137 11 9 -2
138 12 41 29
139 13 21 8
140 14 21 7
141 14 27 13
142 22 23 1
143 12 21 9
144 12 21 9
145 11 24 13
146 10 21 11
147 9 21 12
148 20 78 58
149 17 80 63
150 11 78 67
151 11 80 69
152 15 80 65
153 17 78 61
154 11 80 69
155 20 80 60
156 22 77 55
157 5 28 23
158 19 28 9
159 17 25 8
160 14 31 17
161 8 26 18
162 11 26 15
163 13 40 27
164 14 26 12
165 5 25 20
166 7 11 4
167 6 7 1
168 19 23 4
169 10 31 21
170 22 25 3
171 14 29 15
172 10 26 16
173 18 40 22
174 19 28 9
175 13 33 20
176 17 29 12
177 9 31 22
178 19 34 15
179 17 25 8
180 12 32 20
181 21 25 4
182 18 26 8
183 6 29 23
184 12 28 16
185 21 26 5
186 14 28 14
187 7 24 17
188 5 57 52
189 16 27 11
190 20 17 -3
191 9 79 70
192 12 28 16
193 2 34 32
194 7 NA NA
195 9 28 19
196 5 21 16
197 13 26 13
198 14 27 13
199 7 81 74
200 15 21 6
201 12 22 10
202 20 24 4
203 19 24 5
204 17 34 17
205 12 23 11
206 11 46 35
207 17 30 13
208 18 30 12
209 5 20 15
211 23 30 7
212 4 28 24
214 24 31 7
215 6 20 14
216 6 23 17
217 12 25 13
218 17 28 11
219 9 26 17
220 11 26 15
221 16 35 19
222 17 35 18
223 17 36 19
224 11 34 23
225 21 30 9
226 17 32 15
228 6 28 22
229 17 25 8
230 17 28 11
231 9 27 18
235 19 31 12
236 7 80 73
237 14 23 9
238 7 20 13
239 14 81 67
240 9 81 72
241 12 80 68

3.2. Hierarchical Modelling of Species Communities

All variables

Presence probability of significative taxa:

Prediction of specific richness:

Difference between predicted and observed taxa richness
station_id observed predicted difference
127 1 1 0
128 12 2 -10
129 15 2 -13
130 4 4 0
131 10 2 -8
132 10 3 -7
134 9 3 -6
135 8 3 -5
136 9 5 -4
137 11 7 -4
138 12 8 -4
139 13 4 -9
140 14 5 -9
141 14 6 -8
142 22 6 -16
143 12 6 -6
144 12 6 -6
145 11 6 -5
146 10 6 -4
147 9 6 -3
148 20 9 -11
149 17 7 -10
150 11 7 -4
151 11 10 -1
152 15 8 -7
153 17 4 -13
154 11 10 -1
155 20 9 -11
156 22 6 -16
157 5 2 -3
158 19 3 -16
159 17 4 -13
160 14 3 -11
161 8 4 -4
162 11 5 -6
163 13 7 -6
164 14 5 -9
165 5 7 2
166 7 5 -2
167 6 4 -2
168 19 3 -16
169 10 10 0
170 22 3 -19
171 14 3 -11
172 10 29 19
173 18 13 -5
174 19 13 -6
175 13 7 -6
176 17 13 -4
177 9 14 5
178 19 13 -6
179 17 10 -7
180 12 12 0
181 21 11 -10
182 18 12 -6
183 6 13 7
184 12 17 5
185 21 10 -11
186 14 12 -2
187 7 3 -4
188 5 5 0
189 16 2 -14
190 20 3 -17
191 9 3 -6
192 12 2 -10
193 2 9 7
194 7 NA NA
195 9 5 -4
196 5 0 -5
197 13 1 -12
198 14 13 -1
199 7 19 12
200 15 10 -5
201 12 10 -2
202 20 3 -17
203 19 5 -14
204 17 4 -13
205 12 5 -7
206 11 4 -7
207 17 7 -10
208 18 17 -1
209 5 2 -3
211 23 8 -15
212 4 15 11
214 24 16 -8
215 6 7 1
216 6 0 -6
217 12 3 -9
218 17 3 -14
219 9 0 -9
220 11 3 -8
221 16 7 -9
222 17 9 -8
223 17 8 -9
224 11 7 -4
225 21 8 -13
226 17 8 -9
228 6 3 -3
229 17 4 -13
230 17 4 -13
231 9 2 -7
235 19 6 -13
236 7 14 7
237 14 4 -10
238 7 4 -3
239 14 11 -3
240 9 14 5
241 12 5 -7

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